This paper presents an innovative method to facilitate making such a plan. Using an algorithm to schedule the starting time of each evacuation group, the method guarantees that the time of completing a large-scale eva...This paper presents an innovative method to facilitate making such a plan. Using an algorithm to schedule the starting time of each evacuation group, the method guarantees that the time of completing a large-scale evacuation is very close to its theoretically shortest evacuation time. Meanwhile, unlike a simultaneous evacuation, during a staged evacuation planned with the proposed method, all evacuees can take the shortest path to a safe exit. Once evacuees start off, they will not suffer any traffic congestion. The above advantages of this innovative method are achieved by using an algorithm with three nested loops. Experiments have been conducted, and their results have validated the proposed method.展开更多
There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key proble...There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.40730526Scientific Research Starting Foundation for Returned Overseas Chinese Scholars(Ministry of Education,China)+2 种基金 the Key Lab of Geographical Information Science under Grant No.KLGIS2011C01Shanghai Natural Science Foundation under Grant No.11ZR1410100Open Grant from Shanghai Key Lab for Urban Ecology and Sustainability(SHUES)
文摘This paper presents an innovative method to facilitate making such a plan. Using an algorithm to schedule the starting time of each evacuation group, the method guarantees that the time of completing a large-scale evacuation is very close to its theoretically shortest evacuation time. Meanwhile, unlike a simultaneous evacuation, during a staged evacuation planned with the proposed method, all evacuees can take the shortest path to a safe exit. Once evacuees start off, they will not suffer any traffic congestion. The above advantages of this innovative method are achieved by using an algorithm with three nested loops. Experiments have been conducted, and their results have validated the proposed method.
基金Project supported by the Key Research Projects of the Central University of Basic Scientific Research Funds for Cross Cooperation,China(No.201510-02)the Research Fund for the Doctoral Program of Higher Education,China(No.2013007211-0035)the Key Project in Science and Technology of Jilin Province,China(No.20140204088GX)
文摘There are many bottlenecks that limit the computing power of the Mobile Web3 D and they need to be solved before implementing a public fire evacuation system on this platform.In this study,we focus on three key problems:(1)The scene data for large-scale building information modeling(BIM)are huge,so it is difficult to transmit the data via the Internet and visualize them on the Web;(2)The raw fire dynamic simulator(FDS)smoke diffusion data are also very large,so it is extremely difficult to transmit the data via the Internet and visualize them on the Web;(3)A smart artificial intelligence fire evacuation app for the public should be accurate and real-time.To address these problems,the following solutions are proposed:(1)The large-scale scene model is made lightweight;(2)The amount of dynamic smoke is also made lightweight;(3)The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method.We propose a real-time fire evacuation system based on the ant colony optimization(RFES-ACO)algorithm with reused dynamic pheromones.Simulation results show that the public could use Mobile Web3 D devices to experience fire evacuation drills in real time smoothly.The real-time fire evacuation system(RFES)is efficient and the evacuation rate is better than those of the other two algorithms,i.e.,the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.
